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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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employment may be available at the end of the initial 2.5 years. The successful candidate will conduct research in the area of Artificial Intelligence and Data Analysis in relation to topics such as graph and
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analysis of data. Prepares appropriate and understandable representations of data such as graphs, charts, tables, statistical summaries, etc. Contributes to the preparation of scientific manuscripts by
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learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph neural networks, transformers) for cross-omics data representation and feature extraction. Apply multi-view
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Responsibilities Research: - Conduct research in AI reasoning, semantic modeling, and knowledge graph development. - Develop and optimize graph databases for structured knowledge representation. - Apply neural
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expressions when the matrix sizes are unknown at compile-time. The project aims to address the problem using e-graphs. An e-graph is a data structure commonly used in automated theorem provers and recently
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Shai Evra Graph theory, representation theory, number theory Yoel Groman Symplectic Geometry Adi Glucksam Complex analysis, Potential theory, and Dynamics Or Hershkovits Geometric Analysis
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, immunofluorescence. • Experience analyzing, graphing and interpreting research results • Experience with oral and written communication of scientific results • Mentoring and leadership potential • Familiarly with
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well as methods for inferring genealogical structures such as ancestral recombination graphs (ARGs) and leveraging them to study heritable traits and human evolution. Applicants should hold a be near completion of
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well as in designing coordination strategies for them. Our recent work on RL and graph neural networks (GNNs) demonstrate some of our key research directions relevant for this position. A high degree of self